import os
import model

import tensorflow as tf

import input_data

data=input_data.read_data_sets('MNIST_data',one_hot=True)

with tf.variable_scope("convolutional"):
    x=tf.placeholder(tf.float32,[None,784],name='x')
    keep_prob=tf.placeholder(tf.float32)
    y,variables=model.convolutional(x,keep_prob)

#train

y_=tf.placeholder(tf.float32,[None,10],name='y')
cross_entropy=-tf.reduce_sum(y_*tf.log(y))
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

saver=tf.train.Saver(variables)

with tf.Session() as sess:
    merged_summary_op=tf.summary.merge_all()
    summary_writer=tf.summary.FileWriter('/tmp/mnist_log/1',sess.graph)
    summary_writer.add_graph(sess.graph)
    sess.run(tf.global_variables_initializer())

    for i in range(1000):
        batch=data.train.next_batch(50)
        if i%100==0:
            train_accuracy=accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_prob:1.0})
            # print("step %d,training accuracy $g"%(i,train_accuracy))
            print ("step %d, training accuracy %g" % (i, train_accuracy))
        sess.run(train_step,feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5})
    print(sess.run(accuracy, feed_dict={x: data.test.images, y_: data.test.labels,keep_prob:1.0}))
    path=saver.save(
        sess,os.path.join(os.path.dirname(__file__),'data','convalutional.ckpt'),
        write_meta_graph=False,write_state=False)

    print("Saved:",path)
